Data analytics typically refers to the science that incorporates various disciplines including, but not limited to, data engineering, mathematics, statistics, computing, and domain-specific expertise. A data scientist thus is one who practices some or all aspects of data analytics in attempting to solve complex data problems.
Conventional data analytics solutions are becoming more and more limited due to the increasing sizes and varying structures of data sets that such solutions are applied against. Such limitations include the lack of ability to adequately estimate the cost of the data analytics solution, the inflexibility and lack of optimality of the solution once it is defined, and the difficulty of putting the solution into operation. These negative factors result in a computing system provisioned to implement the data analytics solution that is costly, that does not adequately handle the data it was intended to handle, that is not optimal, and that is not adequately or beneficially operationalized.
Accordingly, improved data analytics techniques are needed that enable business users and data scientists to develop and implement data analytics more easily and efficiently.